Course Outline
Course Outline Training Proposal
Day 1 - Foundations of AI and Python for Data Workflows
• Overview of the artificial intelligence and machine learning ecosystem
• The role of AI in contemporary data engineering
• Refresher on Python essentials for AI applications
• Data manipulation using pandas and NumPy
• Introduction to APIs and JSON data processing
• Practical exercise: loading and transforming datasets
Day 2 - Machine Learning Fundamentals for Practitioners
• Concepts of supervised and unsupervised learning
• Techniques for feature engineering and data preparation
• Basic model training using scikit-learn
• Model assessment and performance metrics
• Overview of model deployment principles
• Hands-on session: creating a simple predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language models and their operational mechanics
• Tokenization, context windows, and inherent limitations
• Principles and techniques of prompt design
• Zero-shot and few-shot prompting methods
• Strategies for prompt evaluation and iterative improvement
• Practical prompt engineering activities
Day 4 - Building AI Applications with LLMs
• Utilising LLM APIs within Python
• Concepts of structured outputs and function calling
• Development of chat-based and task-oriented applications
• Introduction to retrieval-augmented generation
• Linking LLMs with external data sources
• Mini-project: constructing a basic AI assistant
Day 5 - Productionising AI Solutions
• Architecting scalable AI workflows
• Embedding AI into data pipelines
• Monitoring and enhancing model performance
• Cost optimisation and API usage strategies
• Security protocols and responsible AI practices
• Final project: developing a comprehensive end-to-end AI solution
Testimonials (2)
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
